Can Privacy & Data Sharing

Coexist?

Scott AlbinHead of Ecosystem ServicesData Republic

A conversation with

Moderated by Mike Meriton

Co-Founder & COO, EDM Council

• Joined EDM Council full-time 2015 to lead Industry Engagement

• EDM Council Co-Founder & First Chairman (2005-2007) –

Finance Board Chair (2007-2015)

• Former CEO GoldenSource (2002-2014) – an original IBM Global

MDM Company

• Former President of CheckFree CFACS (Compliance &

Reconcilement Solutions)

• Former Executive for D&B Software and Oracle

• FinTech Innovation Lab – Executive Mentor (2011 – Present)

Hosted by Scott AlbinHead of Ecosystem Services

• Focused on growing the Data Republic ecosystem of banks,

telcos, airlines, retailers, and others contributing to the Data

Economy.

• Former Southeast Asia Data & Analytics Consulting Leader for

PwC Singapore, advising on data strategy, use of advanced

analytics, AI and data sharing and commercialization.

• Has worked across financial services (retail banking, wealth and

insurance), public sector, travel & transportation, retail, energy

and healthcare.

• He has lived and worked across a range of markets

including the US, Australia and Southeast Asia

Can Privacy & Data Sharing Coexist?

1. Foundations of the emerging data economy

2. The perceived data privacy vs innovation tradeoff

3. Real-world data innovation and collaboration case studies

4. Data management considerations for external collaboration

5. Scaling projects with secure technology from Data Republic

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Foundations of the emerging data economy

1

The value of data is climbing exponentially- and quickly

$65 million10% increase in data accessibility can mean $65M increase for Fortune 1000 companies.

Forrester

10%

In 2022, data initiatives are predicted to generate

$274.3 billionForrester

Datavalue

+

But up to

73%

of data never gets used or analyzedForrester

Data liquiditycan turn data into a valuable resource

MatureImmature

Imm

atu

reM

atu

reP

riv

acy

La

w F

ou

nd

ati

on

Data Portability / Utility

Competing agendas for strengthened privacy and open data regulation are being implemented around the world

Strengthened Privacy

● GDPR/PDPA/CCPA

● Data Sovereignty

● ACCC Unbundled

Consent

Open Data

● EU Open Data Strategy

● Open Banking

● Data Portability

● Consumer Data Right

$2.9 trillionAI Augmentation will create $2.9 Trillion of business value in 2021

Gartner

AI investments are set to boost revenue by over 30% over the next four years.

Accenture

AI Value

$

30%

One example of data utility: the application of technology and algorithms, which are getting moresophisticated at an incredible pace

AI and algorithms need data like a rocket needs fuel

Data liquidity + AI and

ML =New

business value

In order to innovate andcreate value, you need to connect your data to the outside world + keep it safe

Data

Skillsets

Algorithms

3rd parties

Data Products

2 The perceived data privacy vs. innovation tradeoff

The myth

Privacy vs. Innovation

SecurityIdentity

TrustGovernance

ConsentTransparency

Data sharingCollaborationOpen innovationLiquidityValueSocial good

The reality today is very different

Privacy and Innovation

SecurityIdentity

TrustGovernance

ConsentTransparency

Data sharingCollaborationOpen innovationLiquidityValueSocial good

There are five key problems to solve that bridge the ‘tech gap’ and enable scalable data collaboration.

Key technology requirements to enable enterprise organisations to balance privacy and innovation

Time tovalue

Privacy and consent

Access vsgovernance

Secureanalytics

Scalability

● Standardised governance workflows

● Common legal framework

● No PII● Decentralised

matching● Models for

Consent management

● Configurable access controls

● Simplified project licensing

● Audit trails

● Quarantined analytics workspaces

● Agnostic tooling● Output

governance

● Secure data source integration

● Flexible workspace deployment

● Decentralised PII protection

3 Real-world data innovation and collaboration case studies

The data innovation and collaboration maturity curve

Innovationat scale

Internaloptimization

Data collaboration

Data commercialization

Dataecosystem

Case study: Rapid evaluation of AI and ML solutions

Innovation at Scale

Who: Global Health Insurance Company

Goal: ● Collaborate and innovate ● Quickly engage and evaluate new capability

providers, those with potentially valuable AI or ML solutions

● Host internal data challenges or datathons including their internal data science team

Solutions:● Improved care delivery● Operational cost reduction to mitigating

disease recurrence● Developing targeted treatment plans for at-

risk populations

Impact:

● $5M+ in recognizable operational return from a single partner project by deployment of a Sandbox-built and maintained AI model

● Crowdsourced innovation spurs cognitive diversity and returns equivalent productivity of 1 FTE in as little as 35 days

● 48 hour turnaround from receipt of legal documents to data access in a Workspace

● 25+ organizations evaluated within 9 months

Case study: Safely match customers across datasets for new customer acquisition

Data Collaboration

Who: Financial Institution and Loyalty Program

Goal: ● New customer acquisition. ● Marketing initiatives to introduce Loyalty

customers to the financial services offering.

Solution: ● De-identified, matched customer records

analysed● Boolean logic for suppression list of existing

customers● ML techniques applied to identify

characteristics of a high value customer● Marketing campaigns targeting new

customers launched

Impact:

● Rapid analysis of a matched dataset that previously could not be combined

● Application of ML techniques surfaced opportunity to target customers better

● 1,700 new customers acquired in the first 2 weeks of launch of campaign

Data management considerations for collaboration

4

Audit

Legal People Use Security

Data Output

Data Republic has identified seven types of controlsfor governance of data collaboration projects

Additional information on Data Republic Seven Controls Framework available on request

Data with PII requires additional considerationsand measures

Separate PII and non-PII

Tokenize attribute data (“pseudonymisation”)

Transformation (e.g. aggregation, differential privacy)

Control (e.g. access, disclosure)

Basic approaches to privacy preservation

Consent to use customer data has nuanced considerations

Consent considerations for customer data

Data use scenario Category description General consent required

Aggregate / non-matched Receiving or providing data to a third party at an aggregate level (anonymised, non-personal, non-identifiable, no “pink ferrari” possibility)

Not required (but obtaining ‘Use’ consent is best practice)

Matched-in Receiving data at a personal level against an identifiable individual

Required to obtain ‘Collection’ consent

Matched-out Providing data to a third party at a personal level against an identifiable individual

Required to obtain ‘Disclosure’ consent

Other considerations...

Regulatory Jurisdiction Anonymisation / De-identified Exemptions & the grey areas

1 Start with a plan and clear use cases

2 Keep it simple (to start)

3Importance of internal and external readiness

4 Lead, don’t follow

Lessons learned

Scaling projects with secure technology from Data Republic

5

The safe, secure and scalable way to innovate and collaborate with data

Senate PlatformThe trusted platform for governing inter-organisational data collaboration and innovation

Senate MatchingPrivacy preserving , patented matching technology that does not let Personal Information (PI) leave your secure systems

Data Republic EcosystemJoin, create or expand an ecosystem of trusted data partners, consumers and providers.

Scan the QR code to watch a demo of the Senate platform

Governed projects are performed in two ways on the Senate platform

Innovation Sandbox

Secure sandbox for leaders to accelerate innovation agendas, without compromising data security or privacy.

Data Collaboration Suite

Confidently launch secure projects with data from multiple parties, with governance and security all under control.

● Create new data insights with your partners by matching customers and combining insights to tailor offers and personalise experience

● Develop new revenue streams by enabling ‘data apps’ to be created with your data and partners data

● Conduct M&A due diligence

● Host hackathons using your sensitive data without the risk of data exposure or PI leakage

● Invite startups, academics or high tech companies (AL/ML) to demonstrate their algorithms, IP or skillsets on your data

● Securely expose sensitive data to partners / suppliers to improve efficiencies

Data Republic

Trusted by 150+ organisationsacross Singapore, Australia and the USA

Top 5US Insurer

Top 5 SG Insurer

Questions?

FOR MORE INFORMATION:

Scott Albin

Head of Ecosystem Services

Data Republic

scott@datarepublic.com

Full presentation with

appendix of additional

information will be sent

via email.

Appendix

1 Step-by-step guide for data innovation and collaboration with Data Republic

APPENDIX

Innovation at Scale

Workflows to Access Data

Legals Data PrepSecure

WorkspaceAnalytics

& AI

Deploying ‘Into the

wild’

Monitor

Iterate & Improve

Data Preparation

The right data, to right people;

at the right time.

Controlled AccessBYO tools and flexible

infrastructure.

Monitoring and Optimising

Improving solutions overtime.

Decision frameworkThe who, what, when,

where, why, how.

A

Enterprise Organisation

ML

AIAI

One to Many

APPENDIX

Data Collaboration

Workflows to Access Data

LegalsDe-identifyMatching

Re-IDRisk

Solutions

Secure Workspace

Data PreparationThe right data, to right people;

at the right time.

Decision frameworkThe who, what, when,

where, why, how.

Controlled AccessBYO tools and flexible

infrastructure.

Approved Output

Insight ExtractedApproved under data

license terms.

One to OneA

Enterprise Organisation

B

Enterprise Organisation

APPENDIX

Data innovation deep dive2

APPENDIX

The data innovation imperative

Assessing external technologies and tools is essential for innovation and transforming companies digital capabilities.

Many enterprises do not have the data expertise internally, and need to assess external capabilities and tools to drive their business forward.

84%

of executives say that innovation is important to their growth strategy

McKinsey

Yet for business owners or innovation managers, today’s solutions for testing data tools are costly, time-intensive and risky.

APPENDIX

Your company data has an abundance of potential value

Realized value

Performance gap

Probable value

Potential value

Vision gap

Gartner APPENDIX

Senate Innovation Sandbox

wo

rksp

ace

Se

na

te

Your org’s environment

Senate project setup

Approved output

Enable innovation analysis by deploying:

Partner, alternative or 3rd party data

AI/ML/deep learning solution companies

Bespoke tool sets & programs

External data talent

Approved data packages uploaded

to Workspace

Data scientists analyze data in quarantined

workspace

Data license approved

Workspace launchedData uploaded as a package

Database Contributor node

Customer data is de-identified using Senate Matching

C

T

APPENDIX

Case study: Innovation

Who: Global Health Insurance Company

Goal: • Collaborate with new partners and drive innovation.• Quickly engage and evaluate new capability providers, those

with potentially valuable AI or ML solutions (i.e. train a machine learning algorithm to determine which patients in a dataset are most likely to return to a hospital within 30 days of discharge).

• Host internal data challenges or datathons including their internal data science team.

Problem: Data privacy and security concerns.

Solution: Data Republic for neutral infrastructure and legal framework to facilitate a sandboxing solution, where the companies de-identified raw data could be made available for secure discovery by external organizations.

How it worked:• 10-20Tb of de-identified health data under strict infosec and

privacy rules onboarded to Senate.• Fast deployment of restricted views of this data within Data

Republic’s secure workspace.• Provision of Partner algorithm to the data under a

permitted use license structure.• Risk team access to Partner evaluation with minimal

training and effort to test and review output.

Impact:

Successful ROI from new risk models.

Global engagement model.

Provided platform for expansion into next generation use cases.

APPENDIX

Case study: Innovation

Who: Global Health Insurance Company

Goal: A health insurer with a global footprint leverages Data Republic’s Senate technology to innovate on 10+ years of certified, de-identified patient and clinical research data to solve real-world problems in healthcare.

Solutions:● Improved care delivery● Operational cost reduction to mitigating disease

recurrence● Developing targeted treatment plans for at-risk

populations

Impact:

● $5M+ in recognizable operational return from a single partner project by deployment of a Sandbox-built and maintained AI model

● Crowdsourced innovation spurs cognitive diversity and returns equivalent productivity of 1 FTE in as little as 35 days

● 48 hour turnaround from receipt of legal documents to data access in a Workspace

● 25+ organizations evaluated within 9 months

APPENDIX

Case study: Innovation

Who: Bank and AI Platform DataRobot

Goal: Evaluate capabilities of an AI platform. New technologiesand techniques to transform the Banks model for evaluating consumer retail risk.

Problem: To bring the DataRobot software directly into the Bank would be a time-intensive and expensive exercise.

Solution: Data Republic for secure, neutral infrastructure and legal framework to facilitate a sandboxing solution.

How it worked: The DataRobot software was installed in Data Republic’s secure workspace, to be accessed and trialled by the Bank’s analysts.

The Bank used their consumer credit application data as input into the DataRobot tool to consider more sophisticated techniques to evaluate retail risk:

• Vast range of linear models.• Hyperparameter tuning options.

DataRobot provided comprehensive model explanations saving time for the Bank’s analysts.

Impact:

AI methods confidently and easily applied.

Insights developed in hours rather than months.

APPENDIX

Case study: Innovation

Who: Bank

Goal: To use research data from Roy Morgan as sample-based validation of their value models.• Secure, de-identified analysis of a matched dataset covering

research data and bank data.• Restriction of the data license to exclude any row level extracts • Conduct analytics to compare the value segments used by the

bank of a sample of matched records, enriched by Roy Morgan research.

Solution: Secure analytics Workspace provided with attribute data from Roy Morgan and the bank, and matched data tables enabled for analysis through Senate Matching.• Matched data tables de-identified and provided the key to

delivering a multi-company view of each customer that exists within both datasets.

• Customer value model insights delivered to adhere with the data license agreed between two parties.

Impact:

Validation of the accuracy of the bank’s value segmentation.

New insights into how to make the bank approach more encompassing in the future, looking at fresh indicator variables.

Deepened collaboration framework between bankand research company.

APPENDIX

Case study: Innovation

Who: Temasek and StartupX

Goal: • To ideate and build solutions for a better and more sustainable

future through the world’s first global sustainability hackcelerator.

• A Datathon segment allowed participants to access synthetic data and analytics toolkits to build innovative data-driven solutions focused on bettering health outcomes and financial well-being.

Solution: Data Republic provided strategic advisory services on Datathon event preparation, governance of Datathon licensing, dataset de-identification and loading, as well as participant preparation.• A Datathon Project capturing all licensing terms for combined

datasets and participants facilitated via the Senate Platform. • Teams securely accessed and analysed synthetic datasets and

design innovative data-driven solutions through Senate secure workspaces.

• A dedicated Data Republic support team were on the ground at the event to give expert advice and guidance.

How it worked:• 3 day event• 3 months in the making• 52 hours of hacking • 60 Datathon participants• 100+ Hackathon participants

Impact:

The winning team delivered a data-driven solutionthat predicts and identifies populations with ‘at risk’ health, based on their supermarket and banking transactions.

APPENDIX

Case study: Innovation

Who: Bank

Partners: Artificial Intelligence, Machine Learning, Data Processing and Data Visualization firms

Goal: To provide granular data to external capability partners in a governed, safe and regulatory compliant way.

Solution: Data Republic provided strategic advisory services on Datathon event preparation, governance of Datathon licensing, dataset de-identification and loading, as well as participant preparation.• A Datathon Project capturing all licensing terms for combined

datasets and participants facilitated via the Senate Platform. • Teams securely accessed and analysed synthetic datasets and

design innovative data-driven solutions through Senate secure workspaces.

• A dedicated Data Republic support team were on the ground at the event to give expert advice and guidance.

How it worked:• Capability partner, a data visualisation firm, uses Data

Republic to access banking credit transaction data to create insight and visualisation reports over a geo-spacial map for its customers in the government and retail sector.

• Bank used a powered chatbot created by a capability partner, an AI firm, to reduce the number of enquiriescoming into the call centres.The AI firm’s natural language technology automates the customer services process by allowing consumers to request for information using natural language.

• Data Republic provides both the bank and its AI partner with a sandbox to expose actual customer queries to the AI model.

APPENDIX

Case study: Innovation

Who: Top four Australian Bank

Partners: Hyper Anna, an AI platform

Goal: The Bank were looking to provide their Business Banking clients with access to data-driven insights, via an easy-to-use reporting tool.

Problem: Preserving customer data privacy and security were top-of-mind concerns for the Bank.

Solution:Trial the application of a natural language processing system in the provision of insights to clients, leveraging card transaction data.

Hyper Anna provided an enquiry interface for the Business Banking clients, while Data Republic provided the legal frameworkand bespoke infrastructure to enable Hyper Anna to deploy their application.

How it worked:● Cleansed de-identified card transactional data was

licenced from the Bank to Hyper Anna on a recurring basis.

● Bespoke infrastructure ensured that neither project governance nor the utility of the Hyper Anna application were compromised.

● The Hyper Anna enquiry system considered business performance, catchment analysis, benchmarking and customer demographic information, providing practical insights to clients, to enhance business performanceand optimize productivity.

Impact:

● Successfully trialed for 20 clients as part of a pilot project.

● The business intelligence product could be applied to any other bank transactional data.

APPENDIX

Data management considerations continued

3

APPENDIX

Techniques for protecting privacy

De-identification and privacy preserving linkage

• Hashing• Salting• Tokenization

Disclosure risk controls (Sensitive attributes)

• Access controls: Controlled environments, controlled data access

• Output checks

Re-identification risk management

• Transformations: Aggregation, generalization, perturbation (diff. privacy)

• Access controls: Controlled environments, controlled data access

Advanced techniques

• Homomorphic encryption• Confidential computing• Federated analytics

Watch this webinar for a deep dive into the topic: datarepublic.com/webinar-privacy-preserving-matching-techniques APPENDIX

Considerations for cloud

Importance of neutrality in low trust relationships; protection of data and IP

Data at rest versus data in motion

Regulatory restrictions on use of cloud

Future proofing and maximising interoperability

The role of cloud in data collaboration

APPENDIX

How to get started4

APPENDIX

Setting yourself up for accelerated success requires new capabilities

Strategy and Alignment

Business Value and

Metrics Alignment

Data Ecosystem

Strategy

Use Case and Partner

Identification

Use Case and Partner

Sizing and

Prioritization

Internal Readiness

Operating Model

Due Diligence

Data Readiness

System Set Up

Solution Design

External Readiness

Data

Commercialization

Framework

Go-To-Market

Planning

Partner Value

Proposition

Partner Sourcing and

Scaling

Use Cases and

Partner Plan

Development

Execution

Partner Data Sharing

Onboarding Support

Privacy preserving

data license

development

Delivery management

and tracking

Governance

adherence and

privacy validation

Best Practice

Business Value and

Metrics Alignment

Data Ecosystem

Strategy

Use Case and Partner

Identification

Use Case and Partner

Sizing and

Prioritization

APPENDIX

Data Republic provides expertise for organisations throughout this process. We can help you get started.

APPENDIX

Resources

APPENDIX

Resources● Download the Senate platform whitepaper

https://www.datarepublic.com/whitepaper-senate

● Senate platform demo video https://www.datarepublic.com/senate-platform-demo

● Webinar: Understanding privacy preserving matching techniques https://www.datarepublic.com/webinar-privacy-preserving-matching-techniques

● Data Republic resources library

https://www.datarepublic.com/resources/resources-insights

APPENDIX